Evidence map›Paper›PMID 40278856›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Lung lesion detectability on images obtained from decimated and CNN-based denoised [

Daphné Faist, Silvano Gnesin, Siria Medici, Alysée Khan, Marie Nicod Lalonde, Niklaus Schaefer, Adrien Depeursinge, Maurizio Conti, Joshua Schaefferkoetter, John O Prior and 1 more

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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Daphné FaistDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0002-0994-4329
Silvano GnesinInstitute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0002-6547-3824
Siria MediciInstitute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0002-2909-6689
Alysée KhanInstitute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.
Marie Nicod LalondeDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0002-4886-0611
Niklaus SchaeferDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0002-9732-7696
Adrien DepeursingeDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0002-2362-0304
Maurizio ContiSiemens Medical Solutions USA, Inc. 810 Innovation Drive, Knoxville, TN, 37932, USA.
Joshua SchaefferkoetterSiemens Medical Solutions USA, Inc. 810 Innovation Drive, Knoxville, TN, 37932, USA.ORCID 0000-0001-8249-4865
John O PriorDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland. John.Prior@chuv.ch.ORCID 0000-0003-1429-1374
Mario JreigeDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, CH- 1011, Lausanne, Switzerland.ORCID 0000-0003-3918-150X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess feasibility of lung cancer screening, we analysed lung lesion detectability simulating low-dose and convolutional neural network (CNN) denoised [

methodsRetrospectively, we analysed lung lesions on full statistics and decimated [

resultsWe analysed 141 lung lesions from 49 patients across 588 reconstructions. The dichotomised lung lesion malignancy score was significantly different from 10% decimation without denoising (p < 0.029) and from 5% decimation with denoising (p < 0.001). Compared to full statistics, detectability score distribution differed significantly from 2% decimation without denoising (p < 0.001) and from 5% decimation with denoising (p < 0.001). Detectability scores at same decimation levels with or without denoising differed significantly at 10%, 2%, and 1% decimation (p < 0.019); dichotomised scores did not differ significantly. Denoising significantly increased the proportion of lung lesion scores with a high diagnostic confidence (3 and 0) (p < 0.038).

conclusionLung lesion detectability was preserved down to 30% of injected activity without denoising and to 10% with denoising. These results support the feasibility of reduced-activity [

Indexed as

Convolutional Neural NetworksEarly Detection of CancerImage Processing, Computer-AssistedLung NeoplasmsPositron Emission Tomography Computed TomographyAgedDatasets as TopicFeasibility StudiesFemaleFluorodeoxyglucose F18HumansLungMaleMiddle AgedObserver VariationRetrospective StudiesFluorodeoxyglucose F18DenoisingDetectabilityLow-dose PETLung cancer screening

Identifiers

PMID40278856
PMCPMC12491096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.